基于文化算法的表面肌电信号特征选择

摘要

To improve classification accuracy of the surface electromyography ( sEMG)-based prosthesis, this paper proposed a new way to select feature based on cultural algorithm( CA) and used here. It tested its classification performance with linear discrimina analysis ( LDA) . The method used surface differential electrodes to acquire four EMG signals from human body' s upper limbs. Ten healthy subjects participated in the experiment of classification of eight hand motion' s sEMG signals. Test results show that the algorithm can get a good result of classification. Compared with the standard genetic algorithm ( GA) ,it has better search performance.%为了提高假肢控制系统肌电信号的分类准确率,提出一种新的基于文化算法的特征选择方法,通过该方法选择出最佳特征向量,然后用线性分类器检验其分类性能.利用表面差分电极从人体上肢四块肌肉采集四通道的肌电信号,对十个健康受试者进行八个动作的肌电信号模式分类实验,并同时用标准遗传算法来与文化算法作比较.实验结果表明,文化算法与遗传算法相比,特征维数更小,分类准确度更高.